Swift 4D printing of thermoresponsive shape-memory polymers using vat photopolymerization

Abstract Shape-memory polymers (SMPs) are smart materials that have gained significant attention in recent years owing to their widespread application in smart structures and devices. Digital light processing (DLP), a vat-photopolymerization-based technique, is a significantly faster technology for printing a complete layer in a single step. The current study reports a facile and fast method for the 3D printing of SMP-based smart structures using a DLP 3D printer and a customized resin. A liquid crystal (LC, RM257) was combined with the resin to introduce shape-memory properties. The combination of LCs in photocurable resin provides the opportunity to directly 3D-print thermoresponsive structures, avoiding the complexity of SMP resin preparation. The structures were printed with different geometries, and the shape-memory response was measured. Lattice structures were fabricated and programmed to obtain tunable mechanical properties. Furthermore, the strain-sensing response was measured to demonstrate the utility of these lattice structures as smart patches for joint-movement sensing. The SMPs can be prepared conveniently and can potentially be used for various applications, such as smart tools, toys, and meta-material sensors

Direct Ink Writing of Strained Carbon Nanotube-Based Sensors: Toward 4D Printable Soft Robotics

Four-dimensional (4D) printing has attracted significant attention, because it enables structures to be reconfigured based on an external stimulus, realizing complex architectures that are useful for different applications. Nevertheless, most previously reported 4D-printed components have focused on actuators, which are just one part of a full soft robotic system. In this study, toward achieving fully 4D-printed systems, the design and direct ink writing of sensors with a straining mechanism that mimics the 4D effect are explored. Solution-processable carbon nanotubes (CNTs) were used as the sensing medium, and the effect of a heat-shrinkable shape-memory polymer-based substrate (i.e., potential 4D effect) on the electronic and structural properties of CNTs was assessed, followed by their application in various sensing devices. Herein, we reveal that substrate shrinking affords a more porous yet more conductive film owing to the compressive strain experienced by CNTs, leading to an increase in the carrier concentration. Furthermore, it improves the sensitivity of the devices without the need for chemical functionalization. Interestingly, the results show that, by engineering the potential 4D effect, the selectivity of the sensor can be tuned. Finally, the sensors were integrated into a fully 4D-printed flower structure, exhibiting their potential for different soft robotic applications

Graphene and Liquid Metal Integrated Multifunctional Wearable Platform for Monitoring Motion and Human–Machine Interfacing

Motion sensors are an essential component of many electronic systems. However, the development of inertial motion sensors based on fatigue-free soft proof mass has not been explored extensively in the field of soft electronics. Nontoxic gallium-based liquid metals are an emerging class of material that exhibit attractive electromechanical properties, making them excellent proof mass materials for inertial sensors. Here, we propose and demonstrate a fully soft laser-induced graphene (LIG) and liquid metal-based inertial sensor integrated with temperature, humidity, and breathing sensors. The inertial sensor design confines a graphene-coated liquid metal droplet inside a fluidic channel, rolling over LIG resistive electrode. The proposed sensor architecture and material realize a highly mobile proof mass and a vibrational space for its oscillation. The inertial sensor exhibits a high sensitivity of 6.52% m–1 s2 and excellent repeatability (over 12 500 cycles). The platform is fabricated using a scalable, rapid laser writing technique and integrated with a programmable system on a chip (PSoC) to function as a stand-alone system for real-time wireless monitoring of movement patterns and the control of a robotic arm. The developed printed inertial platform is an excellent candidate for the next-generation of wearables motion tracking platforms and soft human–machine interfaces

Graphene and Liquid Metal Integrated Multifunctional Wearable Platform for Monitoring Motion and Human–Machine Interfacing

Motion sensors are an essential component of many electronic systems. However, the development of inertial motion sensors based on fatigue-free soft proof mass has not been explored extensively in the field of soft electronics. Nontoxic gallium-based liquid metals are an emerging class of material that exhibit attractive electromechanical properties, making them excellent proof mass materials for inertial sensors. Here, we propose and demonstrate a fully soft laser-induced graphene (LIG) and liquid metal-based inertial sensor integrated with temperature, humidity, and breathing sensors. The inertial sensor design confines a graphene-coated liquid metal droplet inside a fluidic channel, rolling over LIG resistive electrode. The proposed sensor architecture and material realize a highly mobile proof mass and a vibrational space for its oscillation. The inertial sensor exhibits a high sensitivity of 6.52% m–1 s2 and excellent repeatability (over 12 500 cycles). The platform is fabricated using a scalable, rapid laser writing technique and integrated with a programmable system on a chip (PSoC) to function as a stand-alone system for real-time wireless monitoring of movement patterns and the control of a robotic arm. The developed printed inertial platform is an excellent candidate for the next-generation of wearables motion tracking platforms and soft human–machine interfaces

Artificial visual perception neural system using a solution-processable MoS2-based in-memory light sensor

Abstract Optoelectronic devices are advantageous in in-memory light sensing for visual information processing, recognition, and storage in an energy-efficient manner. Recently, in-memory light sensors have been proposed to improve the energy, area, and time efficiencies of neuromorphic computing systems. This study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal–oxide–semiconductor (MOS) charge-trapping memory structure—the basic structure for charge-coupled devices (CCD)—and showing its suitability for in-memory light sensing and artificial visual perception. The memory window of the device increased from 2.8 V to more than 6 V when the device was irradiated with optical lights of different wavelengths during the program operation. Furthermore, the charge retention capability of the device at a high temperature (100 °C) was enhanced from 36 to 64% when exposed to a light wavelength of 400 nm. The larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al2O3/MoS2 interface and in the MoS2 layer. A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the device. The array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91% accuracy. This study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception, adaptive parallel processing networks for in-memory light sensing, and smart CCD cameras with artificial visual perception capabilities

Graphene and Liquid Metal Integrated Multifunctional Wearable Platform for Monitoring Motion and Human–Machine Interfacing

Motion sensors are an essential component of many electronic systems. However, the development of inertial motion sensors based on fatigue-free soft proof mass has not been explored extensively in the field of soft electronics. Nontoxic gallium-based liquid metals are an emerging class of material that exhibit attractive electromechanical properties, making them excellent proof mass materials for inertial sensors. Here, we propose and demonstrate a fully soft laser-induced graphene (LIG) and liquid metal-based inertial sensor integrated with temperature, humidity, and breathing sensors. The inertial sensor design confines a graphene-coated liquid metal droplet inside a fluidic channel, rolling over LIG resistive electrode. The proposed sensor architecture and material realize a highly mobile proof mass and a vibrational space for its oscillation. The inertial sensor exhibits a high sensitivity of 6.52% m–1 s2 and excellent repeatability (over 12 500 cycles). The platform is fabricated using a scalable, rapid laser writing technique and integrated with a programmable system on a chip (PSoC) to function as a stand-alone system for real-time wireless monitoring of movement patterns and the control of a robotic arm. The developed printed inertial platform is an excellent candidate for the next-generation of wearables motion tracking platforms and soft human–machine interfaces

Graphene and Liquid Metal Integrated Multifunctional Wearable Platform for Monitoring Motion and Human–Machine Interfacing

Motion sensors are an essential component of many electronic systems. However, the development of inertial motion sensors based on fatigue-free soft proof mass has not been explored extensively in the field of soft electronics. Nontoxic gallium-based liquid metals are an emerging class of material that exhibit attractive electromechanical properties, making them excellent proof mass materials for inertial sensors. Here, we propose and demonstrate a fully soft laser-induced graphene (LIG) and liquid metal-based inertial sensor integrated with temperature, humidity, and breathing sensors. The inertial sensor design confines a graphene-coated liquid metal droplet inside a fluidic channel, rolling over LIG resistive electrode. The proposed sensor architecture and material realize a highly mobile proof mass and a vibrational space for its oscillation. The inertial sensor exhibits a high sensitivity of 6.52% m–1 s2 and excellent repeatability (over 12 500 cycles). The platform is fabricated using a scalable, rapid laser writing technique and integrated with a programmable system on a chip (PSoC) to function as a stand-alone system for real-time wireless monitoring of movement patterns and the control of a robotic arm. The developed printed inertial platform is an excellent candidate for the next-generation of wearables motion tracking platforms and soft human–machine interfaces